@inproceedings{yap-etal-2020-adapting,
    title = "Adapting {BERT} for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences",
    author = "Yap, Boon Peng  and
      Koh, Andrew  and
      Chng, Eng Siong",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2020.findings-emnlp.4/",
    doi = "10.18653/v1/2020.findings-emnlp.4",
    pages = "41--46",
    abstract = "Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions. We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. Using the proposed training objective and data augmentation technique, our models are able to achieve state-of-the-art results on the English all-words benchmark datasets."
}